It's worth noting that HBase has the same method of using LWW on column level updates. While this usually is what you want and like Cassandra it gives you the ability to do fast blind writes there are sometimes that you need to make sure you aren't having conflicting writes.
The solution that HBase employs is to have checkAndPut functionality. Basically what this lets you do is write a value and only successfully save the update if a given column is the value you expect.
So for example you could have a "records" table that has a column called "check" whenever you update a record you pull the old one, do whatever processing you want to do on it, set the "check" column to a new UUID and then save it with a checkAndPut where you specify the "check" column in hbase has to be the old check value you read. If any other process wrote to this row then it would have updated the check value with a new UUID and so this checkAndPut will fail thereby detecting the conflict. Now you can repull the row and handle any conflict resolution without blindly overwriting the changes.
Cassandra exposes functionality similar to checkAndPut as Lightweight Transactions: www.datastax.com/dev/blog/lightweight-transactions-in-cassandra-2-0
The solution that HBase employs is to have checkAndPut functionality. Basically what this lets you do is write a value and only successfully save the update if a given column is the value you expect.
So for example you could have a "records" table that has a column called "check" whenever you update a record you pull the old one, do whatever processing you want to do on it, set the "check" column to a new UUID and then save it with a checkAndPut where you specify the "check" column in hbase has to be the old check value you read. If any other process wrote to this row then it would have updated the check value with a new UUID and so this checkAndPut will fail thereby detecting the conflict. Now you can repull the row and handle any conflict resolution without blindly overwriting the changes.